Search Results (219)

High-frequency surface wave radar (HFSWR) often suffers from highly non-uniform, striped ionospheric clutter, which significantly degrades sea-surface target detection performance. To address this challenge, this paper proposes a reduced-dimension space-time adaptive processing (STAP) algorithm based on sparse representation. In this method, a dictionary is first constructed using the Doppler resolution and an appropriate angle interval as the frequency and angle grids, aiming to obtain fully orthogonal clutter atoms. Both the training sample and the cell under test are then sparsely represented over this dictionary to extract consistent clutter atoms. Due to discrepancies between the dictionary atoms and the actual clutter, low-power atoms are deemed unreliable and are discarded via a thresholding procedure. The remaining reliable atoms are used to construct a dimensionality-reduction matrix, thereby obtaining an accurate local clutter-plus-noise covariance matrix. Experimental results on measured data demonstrate that the proposed method effectively suppresses striped ionospheric clutter and enhances target detection performance. Full article

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar offers significant advantages in mainlobe deceptive interference suppression, as its transmit steering vector contains both angle and range information, providing additional degrees of freedom beyond the angular dimension. However, conventional FDA-MIMO radar suffers from insufficient angle-range resolution, which limits its ability to suppress interferences located close to the target. Moreover, it lacks robustness under limited snapshots and parameter mismatch conditions. To address these issues, this paper proposes a robust beamforming method based on the FDA-MIMO radar model. A collocated sparse array with a sinusoidal element spacing offset and a logarithmic frequency offset is adopted to enhance beam resolution and resolve the periodic angle-range ambiguity problem. Based on this model, the interference-plus-noise covariance matrix is reconstructed using two-dimensional Capon spatial spectrum, and the steering vector is corrected via a joint objective function that combines MUSIC orthogonality and the flatness of the covariance residual spectrum. Simulation results demonstrate that, under conditions of near-target interferences, random range-angle errors, and frequency offset errors, the proposed method achieves a signal-to-interference-plus-noise ratio (SINR) close to the ideal value, exhibiting excellent mainlobe interference suppression performance and robustness. Full article

Adaptive beamforming based on polarization-sensitive arrays enables joint spatial–polarization filtering for mainlobe interference suppression, but mainlobe distortion and performance degradation occur when the received data include the desired signal or multiple mainlobe interferences. Accordingly, this paper proposes a mainlobe interference suppression method based on Polarimetric Sparse Iterative Covariance-based Estimation (POL-SPICE) and covariance matrix reconstruction. This method utilizes the POL-SPICE algorithm to accurately estimate the direction of arrival (DOA), polarization, and power parameters. It reconstructs the covariance matrix by nulling the corresponding source power and constructs a feature projection matrix to preprocess the received signal. These eliminate the impact of the desired signal and mainlobe interference components on subsequent joint spatial–polarization domain beamforming, ultimately achieving interference suppression and mainlobe shape preservation. Simulation results illustrate that the proposed method is applicable to scenarios with the coexistence of the desired signal and multiple mainlobe interferences, and its superiority over existing methods is verified. Full article

Reliable, explainable prediction of the maternal mortality ratio (MMR) is challenging in global health because country-level indicators are heterogeneous and missingness is often informative rather than random. This study aims to develop and validate a missingness-aware TabNet (MA-TabNet), an attention-based framework that treats absence patterns as learnable signals while maintaining a stable feature space for country-level MMR forecasting and interpretation. We build a country–year panel from a publicly available global nutrition and health dataset and predict MMR using socioeconomic and health indicators to test whether missingness patterns add predictive signal beyond observed covariates. The model applies a distribution-aware selective masking strategy, adding missingness indicators only for variables with high missing rates; remaining gaps are handled by median imputation, with indicators retained to explicitly encode reporting uncertainty. Country codes and regional groupings are encoded as learnable embeddings, and entmax-based sequential attention is used to improve feature selection via sparse, competition-style masks under correlated determinants. Hyperparameters are tuned using Bayesian optimization, and evaluation follows a temporally realistic protocol (train on earlier years; test on a future held-out year). MA-TabNet achieves a mean absolute error (MAE) of 21.05, root mean squared error (RMSE) of 36.24, and R² of 0.9739, outperforming strong tree-based baselines and improving on the original TabNet while avoiding the training instability observed in some transformer-style tabular models. For transparency, we report attention-derived global and local importance, compare original versus missing-mask features in model importances, and complement these with permutation-based Shapley additive explanation summaries, permutation importance (MAE drop), partial dependence plots for top drivers, and continent-stratified residual analyses to clarify how structural reporting gaps shape predictions and to support trustworthy maternal health monitoring. Overall, these findings suggest that modeling missingness as a measurable reporting signal can yield accurate, auditable forecasts that are better aligned with temporally realistic SDG 3.1 monitoring than “fill-and-forget” preprocessing. Full article

In the USA, agriculture is the largest consumer of freshwater resources, and precision irrigation (PI) can conserve water significantly while maintaining crop yield. Current approaches to soil volumetric water content (VWC) mapping for PI rely on installing a costly soil moisture sensor within each of 4–5 management zones per field. Although this strategy provides temporally dense data, it is spatially sparse. Alternatively, spatially dense remotely sensed data require calibration with in situ soil moisture measurements, which are expensive and labor intensive to obtain. Previous research indicates that soil VWC zones must be regularly reassessed, a process that is impractical without low-cost soil VWC sensors. In anticipation of deploying dense networks of inexpensive soil moisture sensors for PI in large turfgrass fields, we investigate the mapping errors and optimal sampling density required for accurate soil VWC mapping using random forests (RFs) and z-score calibration in two turfgrass sports fields in Utah. Dense sampling of soil VWC was undertaken at 101 and 103 points in each field with a theta probe. These data were systematically sub-sampled to quantify errors in z-score soil moisture maps generated with varying sample sizes. A jack-knife procedure was employed to determine the optimum number of sensors required to produce accurate RF-based soil moisture maps. The RF approach also allows identification of the most influential covariates for soil VWC prediction. For RFs, 21–79 samples were needed to characterize changing spatial patterns in fields with mean absolute errors (MAEs) of 1.39–9.71%, but for most dates only 25–40 samples were needed. The z-score calibration produced MAEs of 1.38–10.44% with as few as 10–15 samples, but the spatial patterns remain static and only the magnitude of values changes. Therefore, using RFs with 40–60 sensors was recommended to allow for accurate mapping despite dropped signals and broken sensors. Full article

Background: Conventional analyses of physiological recovery often rely on discrete metrics that assume independence across time points, thereby ignoring intrinsic temporal continuity and masking substantial interindividual heterogeneity. This proof-of-concept study assesses the efficacy of Functional Data Analysis (FDA) as a promising framework for characterizing individual response dynamics following a functional threshold power (FTP) test. Methods: Physiological time-series data (including blood lactate, heart rate, blood pressure, and glucose levels) collected from 21 trained cyclists (10 professionals, 11 amateurs) were represented as functional objects using FDataGrid on the original sampling grid (0, 3, 5, 10, 20 min), without basis expansion or smoothing. We conducted unsupervised functional clustering (K-means; Fuzzy K-means) and supervised classification (Maximum Depth with Modified Band Depth, K-Nearest Neighbors, Nearest Centroid, functional QDA with parametric Gaussian covariance). Model performance was estimated via Repeated Stratified 5-Fold Cross-Validation with 10 repetitions (50 folds), reporting accuracy, balanced accuracy (mean ± SD), 95% CIs, permutation p-values, and sensitivity/specificity from aggregated confusion matrices. Results: Lactate (CL) and diastolic blood pressure (DBP) provided useful and statistically significant discrimination across several classifiers (e.g., KNN, Nearest Centroid, functional QDA), whereas heart rate showed modest discriminative value and glucose intermediate performance. Unsupervised analyses revealed distinct lactate recovery profiles and graded membership for hemodynamic/metabolic variables, supporting the value of FDA for resolving heterogeneity beyond group-average trends. Conclusions: FDA offers a feasible and informative approach for classifying recovery phenotypes while preserving temporal structure. Findings are promising but should be interpreted with caution due to the small sample size, sparse time points, and the need for external validation in larger, independent cohorts before translation into routine decision-making. Full article

To address unstable localization and sparse mapping for autonomous vehicles operating in GPS-denied and low-visibility environments, this paper proposes GTS-SLAM, a tightly coupled dense visual SLAM framework integrating Generalized Iterative Closest Point (GICP) and 3D Gaussian Splatting (3DGS). The system is designed for intelligent driving platforms such as underground mining vehicles, inspection robots, and tunnel autonomous navigation systems. The front-end performs covariance-aware point-cloud registration using GICP to achieve robust pose estimation under low texture, dust interference, and dynamic disturbances. The back-end employs probabilistic dense mapping based on 3DGS, combined with scale regularization, scale alignment, and keyframe factor-graph optimization, enabling synchronized optimization of localization and mapping. A Compact-3DGS compression strategy further reduces memory usage while maintaining real-time performance. Experiments on public datasets and real underground-like scenarios demonstrate centimeter-level trajectory accuracy, high-quality dense reconstruction, and real-time rendering. The system provides reliable perception capability for vehicle autonomous navigation, obstacle avoidance, and path planning in confined and weak-light environments. Overall, the proposed framework offers a deployable solution for autonomous driving and mobile robots requiring accurate localization and dense environmental understanding in challenging conditions. Full article

Modeling the complex spatio-temporal dynamics of infectious diseases presents a significant computational challenge due to heterogeneous regional interactions, high-dimensional multimodal data streams, and the critical need for algorithmic fairness. This paper proposes a novel computational framework that unifies graph-based spatio-temporal forecasting, anomaly detection, and retrieval-augmented generation (RAG) into a single mathematical architecture. The predictive backbone employs a graph-augmented Temporal Fusion Transformer to capture non-linear temporal dependencies and spatial disease propagation. By formalizing regional topology and mobility flows as a weighted mathematical graph, the model systematically integrates structured epidemiological counts, continuous environmental covariates, and digital trace signals. To address algorithmic bias, we formulate a fairness-aware optimization problem by embedding a specific regularization term into the training objective, which mathematically penalizes disparities in true-positive rates across diverse socio-demographic strata. Furthermore, the numerical outputs and anomaly scores are processed by a large language model equipped with hybrid dense and sparse retrieval to generate interpretable, computationally grounded decision support. Extensive experiments on a longitudinal dataset comprising 62 administrative regions over 260 weeks validate the mathematical robustness of the proposed framework. The graph-augmented architecture improved forecasting accuracy by up to 24% and anomaly detection F1 scores by over 6% compared to state-of-the-art deep learning baselines, while the fairness-regularized loss function reduced the maximum subgroup recall gap by more than 50%. These findings demonstrate that predictive accuracy and algorithmic fairness can be jointly optimized, providing a rigorous computational methodology for spatio-temporal epidemic modeling and AI-driven surveillance. Full article

This paper evaluates an Adaptive LASSO-MGARCH model for multivariate volatility forecasting, with applications in green and conventional bonds, equities, energy commodities, and EU carbon allowances. By introducing coefficient-specific adaptive penalisation directly into the multivariate GARCH variance equations, the model delivers a sparse and data-driven volatility spillover structure while preserving the positive definiteness of the conditional covariance matrix. Using daily data on green and conventional bonds, equities, energy commodities, and carbon allowances, we show that adaptive regularisation substantially reduces model complexity and improves economic interpretability relative to an unpenalised MGARCH benchmark. Out-of-sample forecasting experiments at multiple horizons demonstrate that the Adaptive LASSO-MGARCH model consistently achieves lower covariance forecast losses, and statistical tests based on the White reality check confirm that these improvements are significant across alternative loss functions. Full article

Tightly coupled GNSS/INS is a widely adopted architecture for UAVs and ground vehicles. In this study, a Kalman-filter-based fusion framework integrates inertial data with satellite observables, including pseudorange and Doppler-derived range rate, to sustain precise navigation when GNSS quality degrades. A key bottleneck is that many pipelines rely on fixed or overly simplified measurement-noise covariance models, which cannot track the nonstationary statistics of real observations. To address this issue, we develop an adaptive covariance estimator built on a Transformer enhanced with three modules: a Linear-Attention layer, a Residual Sparse Denoising Autoencoder (R-SDAE), and a lightweight residual channel-attention block (LRCAM). The estimator predicts the measurement-noise covariance online. R-SDAE distills sparse, outlier-resistant features from noisy ephemeris; LRCAM reweights informative channels via residual gating; and Linear Attention preserves long-range spatiotemporal dependencies while reducing attention cost from O(N²) to O(N). A predictive factor further modulates the covariance for improved efficiency and adaptability. Experimental results on real road-test data show that the proposed method achieves sub-meter positioning accuracy in open-sky conditions and preserves meter-level accuracy with improved robustness under GNSS-degraded urban scenarios, outperforming the compared adaptive-filtering baselines and neural covariance estimators and thereby demonstrating superior positioning accuracy and stability. Full article

Komagataeibacter strains are important bacterial cellulose producers, yet closely related isolates can differ in cellulose yield, pellicle properties, and genetic stability during propagation. Such variability suggests that lineage structure and mobile genetic elements both contribute to strain-level genomic divergence. Here, complete genome comparisons were used to integrate vertical relatedness, gene-content structure, cellulose-associated signatures, and mobilome heterogeneity across 22 closed Komagataeibacter assemblies. A maximum likelihood phylogeny inferred from 642 single copy core genes provided the lineage scaffold. An anvi’o pangenome analysis defined a constant core gene cluster component across genomes and a noncore fraction that accounted for most of the genome differences in gene content. Targeted features linked to cellulose biosynthesis and local c-di-GMP-associated context were extracted from each genome. These features captured differences in bcs neighborhood composition and the presence of nearby GGDEF and EAL domain signals. The resulting feature matrix was projected by principal component analysis to summarize between-genome variation. Mobilome profiles were strongly strain dependent. Plasmid homology clustering identified 12 clusters comprising 36 plasmids from 13 genomes, including two dominant clusters of seven and six plasmids. Mash-based distance summaries further distinguished clusters consistent with conserved backbones from clusters consistent with heterogeneous, module-driven relationships. Prophage sequences, assessed as VIBRANT-predicted regions, were widespread but sparse per genome and dominated by medium length fragments. Insertion sequence burden ranged from 50 to 181 elements per genome, indicating substantial differences in transposition-associated sequence content. Pairwise association tests did not support robust cross module covariation beyond expected relationships among pangenome composition metrics at the current sampling depth. Overall, these results provide a complete genome reference framework linking lineage structure and mobilome heterogeneity, and they define reusable resources for comparative studies in bacterial cellulose biotechnology. Full article

Environments with sparse or repetitive geometric structures, such as long corridors and narrow stairwells, remain challenging for LiDAR–inertial simultaneous localization and mapping (LiDAR–IMU SLAM) due to insufficient geometric observability and unreliable data associations. To address these issues, we propose VINA-SLAM, a novel LiDAR–IMU SLAM framework that constructs a unified global voxel map to explicitly exploit structural consistency. VINA-SLAM continuously tracks surface normals stored in the global voxel map using a normal-guided correspondence strategy, enabling stable scan-to-map alignment in degenerate scenes. Furthermore, a tangent-space metric is introduced to supplement missing rotational constraints around planar regions, providing reliable initial pose estimates for local optimization. A tightly coupled sliding-window bundle adjustment is then formulated by jointly incorporating IMU factors, voxel normal consistency factors, and planar regularization terms. In particular, the minimum eigenvalue of each voxel’s covariance is used as a statistically principled planar constraint, improving the Hessian conditioning and cross-view geometric consistency. The proposed system directly aligns raw LiDAR scans to the voxelized map without explicit feature extraction or loop closure. Experiments on 25 sequences from the HILTI and MARS-LVIG datasets show that VINA-SLAM reduces ATE by 25–40% on average while maintaining real-time performance at 10 Hz in the evaluated geometrically degenerate environments. Full article

This paper focuses on variable selection and parameter estimation for mixed-effects Poisson count regression models. To simultaneously select important variables in both fixed effects and random effects, we propose a double-penalized Poisson count regression model with the Smoothly Clipped Absolute Deviation (SCAD) penalty imposed on both components. To estimate the unknown parameters, we develop a new iterative algorithm called the Double SCAD–Local Quadratic Approximation (DSCAD-LQA) algorithm. Under regularity conditions, the consistency and Oracle property of the proposed estimator are established. Simulation studies are conducted under two types of penalty parameter selection criteria: the Schwarz Information Criterion (SIC) and the Generalized Approximate Cross-Validation (GACV). We evaluate the performance of the proposed method under different levels of correlation among explanatory variables and different covariance structures of random effects. Comparisons are also carried out with the non-penalized model, the single-penalized model, and the double LASSO-penalized model. The results demonstrate that the proposed double SCAD penalty method performs better than the other three methods in terms of important variable selection and coefficient estimation, and is especially effective for sparse models. Full article

Gas analyzers in post-combustion CO₂ capture plants are accurate but slow and sequential, yielding sparse, non-synchronous concentration records across absorber stages. We address this missing-data problem by reconstructing continuous CO₂ profiles with Moving Horizon Estimation (MHE) constrained by a mechanistic absorber model and available measurements; these MHE reconstructions are used as supervisory labels to train an end-to-end Stacked Denoising Autoencoder–Gated Recurrent Unit (SDAE-GRU) model. At run time, we deploy a hybrid soft sensor using the SDAE-GRU together with the mechanistic model and fuse their open-loop predictions via covariance-weighted blending with Gaspari-Cohn localization. We validate this approach on a pilot-scale MEA absorber using data from seven pilot runs conducted at distinct operating conditions, using datasets 1–5 for training/tuning and 6–7 for blind validation. On the blind validation runs, the hybrid estimator achieves a MAPE of 3.79% for stage-wise CO₂ predictions (averaged over all stages and time samples), outperforming both constituents evaluated standalone: 7.86% for the GRU-only soft sensor and 6.79% for the mechanistic model. Because MHE is used only offline to generate labels and to estimate model-error covariances, the deployed estimator is lightweight and suitable for online monitoring. Full article

Unmanned aerial vehicle (UAV) swarms can form sparse virtual antenna arrays (VAAs) for airborne sensing and communications, but their beamforming performance is highly vulnerable to quasi-static formation drift and the limited number of snapshots available within each coherent processing interval. This paper proposes a beacon-aided self-calibration and robust beamforming framework for narrowband UAV-swarm uplinks in strong-interference, low-snapshot regimes. We consider one signal of interest (SOI) and multiple co-channel interferers characterized by their coarse direction-of-arrival (DOA) information. The key idea is to exploit a single dominant non-SOI emitter as a strong calibration source (beacon) to learn the quasi-static geometry drift from data. First, the beacon spatial signature is extracted from the sample covariance matrix via eigenvector–steering-vector alignment, and a correlation-based gate is used to decide whether geometry calibration is reliable. When the gate is passed, the inter-UAV position drift is estimated from element-wise steering ratios to build a calibrated array manifold. Second, using the calibrated steering vectors and coarse DOA information, the interference-plus-noise covariance matrix (INCM) is reconstructed through a low-dimensional non-negative power fitting with mild diagonal loading. Finally, a geometry-aware minimum-variance distortionless response (MVDR) beamformer is designed based on the reconstructed INCM. Simulations on coprime-inspired UAV formations with a single dominant interferer show that the proposed scheme recovers most of the SINR loss caused by geometry mismatch and consistently outperforms baseline MVDR, worst-case MVDR, a recent covariance-reconstruction baseline, and URGLQ in the low-snapshot regime. For example, in a representative setting with $N_{\mathrm{uav}}=7$, ${\sigma }_p=0.10$, ${\mathrm{INR}}_c=30$ dB, and $L=10$, the proposed method achieves approximately 14 dB output SINR at ${\mathrm{SNR}}_{\mathrm{in}}=10$ dB, outperforming nominal SCM-MVDR by about 13 dB and approaching a genie-aided MVDR bound within a few dB, while retaining a computational complexity comparable to standard MVDR. Full article